Advertisement

Accurate Positioning and Orientation Estimation in Urban Environment Based on 3D Models

  • Giorgio GhinamoEmail author
  • Cecilia Corbi
  • Piero Lovisolo
  • Andrea Lingua
  • Irene Aicardi
  • Nives Grasso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

This paper describes a positioning algorithm for mobile phones based on image recognition. The use of image recognition based (IRB) positioning in mobile applications is characterized by the availability of a single camera for estimate the camera position and orientation. A prior knowledge of 3D environment is needed in the form of a database of images with associated spatial information that can be built projecting the 3D model on a set of synthetic solid images (range + RGB images). The IRB procedure proposed by the authors can be divided in two steps: the selection from the database of the most similar image to the query image used to locate the camera and the estimation of the position and orientation of the camera based on available 3D data on the reference image. The MPEG standard Compact Descriptors for Visual Search (CDVS) has been used to reduce hugely the processing time. Some practical results of the location methodology in outdoor environment have been described in terms of processing time and accuracy of position and attitude.

Keywords

Image recognition based location Visual search Positioning Smartphones Low cost 

References

  1. 1.
    Nishkam, R., Pravin, S., Andrew, F., Ahmed, E., Liviu, I.: Indoor localization using camera phones. In: Mobile Computing Systems and Applications (2006)Google Scholar
  2. 2.
    Mautz, R., Tilch, S.: Survey of optical indoor positioning systems. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), September 21-23, 2011Google Scholar
  3. 3.
    Biswas, J., Veloso, M.: WiFi localization and navigation for autonomous indoor mobile robots. In: International Conference on Robotics and Automation (2010)Google Scholar
  4. 4.
    Chung, L., Donahoe, M., Schmandt, C., Kim, I., Razavai, P., Wiseman, M.: Indoor location sensing using geomagnetism. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, pp. 141–154 (2011)Google Scholar
  5. 5.
    Schneegans, S., Vorst, P., Zell, A.: Using RFID snapshots for mobile robot self-localization. In: European Conference on Mobile Robots (2007)Google Scholar
  6. 6.
    Hong-Shik, K., Jong-Suk, C.: Advanced indoor localization using ultrasonic sensor and digital compass. In: International Conference on Control, Automation and Systems (2008)Google Scholar
  7. 7.
    Kitanov, A., Biševac, S., Petrović, I.: Mobile robot self-localization in complex indoor environments using monocular vision and 3D model. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Zürich, Switzerland (2007)Google Scholar
  8. 8.
    Piras, M., Dabove, P., Lingua, A.M., Aicardi, I.: Indoor navigation using smartphone technology: a future challenge or an actual possibility? In: IEEE/ION Position, Location Proceedings of the and Navigation Symposium, May 5-8, 2014Google Scholar
  9. 9.
    Lingua, A.M., Aicardi, I., Ghinamo, G., Francini, G., Lepsoy, S.: The MPEG7 visual search solution for image recognition based positioning using 3D models. In: Proceedings of the 27th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2014), September 8-12, 2014Google Scholar
  10. 10.
    CDVS. ISO/IEC DIS 15938-13 Compact Descriptors for Visual Search (2014)Google Scholar
  11. 11.
    McGlone, C. (ed.): Manual of Photogrammetry, 5th edn., pp. 280–281. ASPRSGoogle Scholar
  12. 12.
    Bornaz, L., Dequal, S.: A new concept: the solid image. In: CIPA 2003 Proceedings of XIXth International Symposium, pp. 169–174 (2003)Google Scholar
  13. 13.
    PCT/EP2011/050994 Method and system for comparing imagesGoogle Scholar
  14. 14.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International, Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, March 2004Google Scholar
  16. 16.
    Karara, H.M. (ed.): Non Topography Photogrammetry, 2nd edn., pp. 46–48. ASPRSGoogle Scholar
  17. 17.
    De Agostino, M., Lingua, A., Marenchino, D., Nex, F., Piras, M.: GIMPHI: a new integration approach for early impact assessment. Applied Geomatics 3(4), 241–249. ISSN 1866-9298Google Scholar
  18. 18.
    Fusiello, A.: Visione computazionale. Tecniche di ricostruzione tridimensionale (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Giorgio Ghinamo
    • 1
    Email author
  • Cecilia Corbi
    • 1
  • Piero Lovisolo
    • 1
  • Andrea Lingua
    • 2
  • Irene Aicardi
    • 2
  • Nives Grasso
    • 2
  1. 1.Telecom ItaliaTorinoItaly
  2. 2.Department of Environment, Land and Infrastructure Engineering (DIATI)Politecnico di TorinoTorinoItaly

Personalised recommendations